Tag: Model-based

MeDiChI 0.4.1 – Model-Based ChIP-chip Deconvolution Algorithm

MeDiChI 0.4.1

:: DESCRIPTION

MeDiChI is method for the automated, model-based deconvolution of protein-DNA binding (Chromatin immunoprecipitation followed by hybridization to a genomic tiling microarray — ChIP-chip) data that discovers DNA binding sites at high resolution (higher resolution than that of the tiling array itself).

::DEVELOPER

Baliga Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • R package

:: DOWNLOAD

 MeDiChI

:: MORE INFORMATION

Citation

Bioinformatics. 2008 Feb 1;24(3):396-403. Epub 2007 Dec 1.
Model-based deconvolution of genome-wide DNA binding.
Reiss DJ, Facciotti MT, Baliga NS.

MGSA 1.39.0 – Model-based Gene Set Analysis

MGSA 1.39.0

:: DESCRIPTION

MGSA (model-based gene set analysis) is an effective alternative to classical gene set enrichment analysis.

::DEVELOPER

Gagneur lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ WIndows/ MacOsX
  • R package
  • BioConductor

:: DOWNLOAD

   MGSA

:: MORE INFORMATION

Citation

Model-based gene set analysis for Bioconductor.
Bauer S, Robinson PN, Gagneur J.
Bioinformatics. 2011 Jul 1;27(13):1882-3. doi: 10.1093/bioinformatics/btr296

CRC 1.1 – Dirichlet Process Model-based Cluster

CRC 1.1

:: DESCRIPTION

CRC (Chinese Restaurant Cluster) implements a model-based Bayesian clustering algorithm. The cluster assignment procedure can be regarded as following a iterative Chinese restaurant process. This program is designed to cluster microarray gene expression data collected from multiple experiments. missing data is allowed. The program is written in C++, and can be run under Linux, Unix, Windows, MAC OSX operating system as a command line exexutable. CRC has the following features comparing to other clustering tools: 1) able to infer number of clusters, 2) able to cluster genes displaying time-shifted and/or inverted correlations, 3) able to tolerate missing genotype data and 4) provide confidence measure for clusters generated. Here is some more details on why you should try CRC for your microarray data analysis.

CRC Online Version

::DEVELOPER

Steve Qin @ the Center for Statistical Genetics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux /  Windows / MacOsX

:: DOWNLOAD

 CRC

:: MORE INFORMATION

Citation

Qin ZS.
Clustering microarray gene expression data using weighted Chinese restaurant process.
Bioinformatics. 2006 Aug 15;22(16):1988-97. Epub 2006 Jun 9.